Adaptation, Biological; Biological Evolution; Computational Biology; Evolution, Molecular; Genomic Structural Variation; High-Throughput Nucleotide Sequencing; Medical Informatics; Models, Molecular; Molecular Structure; Population
Our research aims at the integration of different areas of expertise to answer important biological questions. To this end we employ and develop mathematical and computer science tool and apply them to biological questions. Recently we have expanded our focus from the reconstruction of the evolutionary history, the development of phylogenetic methods and complex models of sequence evolution to the analysis of large and complex biological and medical data sets most of them generated with high throughput sequencing technologies. In this context we are interested in data management issues especially data compression. We also developed algorithm to quickly and accurately map reads to a reference genome, that are extended to also map bisulfite deep sequencing data and have been recently applied to study changes in the abundance of RNA editing in miRNAs from mice. Finally, we are interested to study the sampling properties of RNAseq experiments. Our research group is also involved in analyses of whole genomes and the development of statistical tools to assign a collection of SNPs to biological pathways or to gene ontologies.
Techniques, methods & infrastructure
Mathematical and computer science tools; Alignments; Statistics of sequence alignment (i.e. mcmcalgn); Sequence evolution; We are developing test statistics to select the "best" model, to detect groups of sequence that evolve differently form the rest of a gene family, Methods to detect the dependency structure among sequence positions in an alignment.
Analysis of large and complex biologica and medical data sets; high through-put sequencing,
Gene trees:We develop efficient heuristic algorithms to reconstruct trees based on sequence data (i.e. IQ-TREE). Population genetic, Complex patterns of evoultion, Species trees, Austrian EMBnet Node,
- Prakash, C. & Haeseler, A.V., 2017. An Enumerative Combinatorics Model for Fragmentation Patterns in RNA Sequencing Provides Insights into Nonuniformity of the Expected Fragment Starting-Point and Coverage Profile. Journal of Computational Biology, 24(3), pp.200-212. Available at: http://dx.doi.org/10.1089/cmb.2016.0096.
- Kalyaanamoorthy, S. et al., 2017. ModelFinder: fast model selection for accurate phylogenetic estimates. Nature Methods, 14(6), pp.587-589. Available at: http://dx.doi.org/10.1038/nmeth.4285.
- Kaiser, T.S. et al., 2016. The genomic basis of circadian and circalunar timing adaptations in a midge. Nature, 540(7631), pp.69-73. Available at: http://dx.doi.org/10.1038/nature20151.
- Gesson, K. et al., 2016. A-type lamins bind both hetero- and euchromatin, the latter being regulated by lamina-associated polypeptide 2 alpha. Genome Research, 26(4), pp.462-473. Available at: http://dx.doi.org/10.1101/gr.196220.115.
- Nguyen, L.-T. et al., 2014. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Molecular Biology and Evolution, 32(1), pp.268-274. Available at: http://dx.doi.org/10.1093/molbev/msu300.